Machine learning determination of atomic dynamics at grain boundaries
Abstract
In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. In this work, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements while finding a large variability within high-energy grain boundaries. As has been found in glasses, the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.
- Publication Date:
- Research Org.:
- Univ. of Pennsylvania, Philadelphia, PA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; Simons Foundation; National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
- OSTI Identifier:
- 1476836
- Alternate Identifier(s):
- OSTI ID: 1596602; OSTI ID: 1856743
- Grant/Contract Number:
- FG02-05ER46199; DMR-1507013; P200A160282; ACI-1053575
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 115 Journal Issue: 43; Journal ID: ISSN 0027-8424
- Publisher:
- National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; machine learning; plasticity; grain boundaries; grain boundaries, crystals, machine learning
Citation Formats
Sharp, Tristan A., Thomas, Spencer L., Cubuk, Ekin D., Schoenholz, Samuel S., Srolovitz, David J., and Liu, Andrea J. Machine learning determination of atomic dynamics at grain boundaries. United States: N. p., 2018.
Web. doi:10.1073/pnas.1807176115.
Sharp, Tristan A., Thomas, Spencer L., Cubuk, Ekin D., Schoenholz, Samuel S., Srolovitz, David J., & Liu, Andrea J. Machine learning determination of atomic dynamics at grain boundaries. United States. https://doi.org/10.1073/pnas.1807176115
Sharp, Tristan A., Thomas, Spencer L., Cubuk, Ekin D., Schoenholz, Samuel S., Srolovitz, David J., and Liu, Andrea J. Tue .
"Machine learning determination of atomic dynamics at grain boundaries". United States. https://doi.org/10.1073/pnas.1807176115.
@article{osti_1476836,
title = {Machine learning determination of atomic dynamics at grain boundaries},
author = {Sharp, Tristan A. and Thomas, Spencer L. and Cubuk, Ekin D. and Schoenholz, Samuel S. and Srolovitz, David J. and Liu, Andrea J.},
abstractNote = {In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. In this work, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements while finding a large variability within high-energy grain boundaries. As has been found in glasses, the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.},
doi = {10.1073/pnas.1807176115},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 43,
volume = 115,
place = {United States},
year = {Tue Oct 09 00:00:00 EDT 2018},
month = {Tue Oct 09 00:00:00 EDT 2018}
}
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https://doi.org/10.1073/pnas.1807176115
https://doi.org/10.1073/pnas.1807176115
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Cited by: 55 works
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